stFemale)Geiser C. Challco geiser@alumni.usp.br
env <- "stFemale"
gender <- "men"
to_remove <- c('S11')
sub.groups <- c("country","age","ed.level","intervention",
"country:age","country:ed.level","country:intervention",
"age:intervention","ed.level:intervention",
"country:age:intervention","country:ed.level:intervention")dat <- read_excel("../data/data-without-outliers.xlsx", sheet = "perform-env.gender-descriptive")
dat <- dat[!dat$study %in% to_remove, ]
leg <- read_excel("../data/data-without-outliers.xlsx", sheet = "legend")## New names:
## • `` -> `...10`leg <- leg[!leg$study %in% to_remove, ]
idx.e <- which(dat$env == env & dat$gender == gender)
idx.c <- which(dat$env == "control" & dat$gender == gender)
data <- data.frame(
study = dat$study[idx.c],
n.e = dat$N[idx.e], mean.e = dat$M[idx.e], sd.e = dat$SD[idx.e],
n.c = dat$N[idx.c], mean.c = dat$M[idx.c], sd.c = dat$SD[idx.c]
)
for (cgroups in strsplit(sub.groups,":")) {
data[[paste0(cgroups, collapse = ":")]] <- sapply(data$study, FUN = function(x) {
paste0(sapply(cgroups, FUN = function(namecol) leg[[namecol]][which(x == leg$study)]), collapse = ":")
})
}
data[["lbl"]] <- sapply(data$study, FUN = function(x) leg$Note[which(x == leg$study)])m.cont <- metacont(
n.e = n.e, mean.e = mean.e, sd.e = sd.e, n.c = n.c, mean.c = mean.c, sd.c = sd.c,
studlab = lbl, data = data, sm = "SMD", method.smd = "Hedges",
fixed = F, random = T, method.tau = "REML", hakn = T, title = paste("Performance for",gender,"in",env)
)
summary(m.cont)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random)
## S1 -0.2867 [-1.2593; 0.6860] 6.7
## S2 -0.0965 [-0.8750; 0.6820] 8.9
## S3 -0.5455 [-1.3658; 0.2749] 8.4
## S4 -1.4272 [-2.3807; -0.4738] 6.9
## S5 0.2143 [-0.4172; 0.8459] 11.1
## S6 0.3687 [-0.2637; 1.0011] 11.1
## S7 -0.0005 [-0.5445; 0.5435] 12.7
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.cont, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = country, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random) country
## S1 -0.2867 [-1.2593; 0.6860] 6.7 Brazil
## S2 -0.0965 [-0.8750; 0.6820] 8.9 Brazil
## S3 -0.5455 [-1.3658; 0.2749] 8.4 Brazil
## S4 -1.4272 [-2.3807; -0.4738] 6.9 Brazil
## S5 0.2143 [-0.4172; 0.8459] 11.1 Brazil
## S6 0.3687 [-0.2637; 1.0011] 11.1 Brazil
## S7 -0.0005 [-0.5445; 0.5435] 12.7 Brazil
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3 China
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6 Brazil
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3 Brazil
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country = Brazil 9 -0.2700 [-0.6548; 0.1149] 0.1063 0.3260 15.41 48.1%
## country = China 1 0.3478 [-0.3995; 1.0950] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 2.20 1 0.1377
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = country, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random) country
## S1 -0.2867 [-1.2593; 0.6860] 6.7 Brazil
## S2 -0.0965 [-0.8750; 0.6820] 8.9 Brazil
## S3 -0.5455 [-1.3658; 0.2749] 8.4 Brazil
## S4 -1.4272 [-2.3807; -0.4738] 6.9 Brazil
## S5 0.2143 [-0.4172; 0.8459] 11.1 Brazil
## S6 0.3687 [-0.2637; 1.0011] 11.1 Brazil
## S7 -0.0005 [-0.5445; 0.5435] 12.7 Brazil
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3 China
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6 Brazil
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3 Brazil
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country = Brazil 9 -0.2700 [-0.6548; 0.1149] 0.1063 0.3260 15.41 48.1%
## country = China 1 0.3478 [-0.3995; 1.0950] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 2.20 1 0.1377
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = ed.level, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random) ed.level
## S1 -0.2867 [-1.2593; 0.6860] 6.7 upper-secundary
## S2 -0.0965 [-0.8750; 0.6820] 8.9 upper-secundary
## S3 -0.5455 [-1.3658; 0.2749] 8.4 upper-secundary
## S4 -1.4272 [-2.3807; -0.4738] 6.9 higher-education
## S5 0.2143 [-0.4172; 0.8459] 11.1 higher-education
## S6 0.3687 [-0.2637; 1.0011] 11.1 higher-education
## S7 -0.0005 [-0.5445; 0.5435] 12.7 unknown
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3 unknown
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6 unknown
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3 upper-secundary
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## ed.level = upper-secundary 4 -0.4244 [-0.7977; -0.0511] 0 0 1.16 0.0%
## ed.level = higher-education 3 -0.2277 [-2.6367; 2.1813] 0.7507 0.8664 10.35 80.7%
## ed.level = unknown 3 -0.1035 [-1.1720; 0.9651] 0.0854 0.2922 3.80 47.4%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 1.42 2 0.4906
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = intervention, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random)
## S1 -0.2867 [-1.2593; 0.6860] 6.7
## S2 -0.0965 [-0.8750; 0.6820] 8.9
## S3 -0.5455 [-1.3658; 0.2749] 8.4
## S4 -1.4272 [-2.3807; -0.4738] 6.9
## S5 0.2143 [-0.4172; 0.8459] 11.1
## S6 0.3687 [-0.2637; 1.0011] 11.1
## S7 -0.0005 [-0.5445; 0.5435] 12.7
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3
## intervention
## S1 Gender-stereotype color, ranking, badges, and avatar
## S2 Gender-stereotype color, ranking, badges, and avatar
## S3 Gender-stereotype color, ranking, badges, and avatar
## S4 Gender-stereotype color, ranking, badges, and avatar
## S5 Gender-stereotype color, ranking, badges, and avatar
## S6 Gender-stereotype color, ranking, badges, and avatar
## S7 Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## intervention = Gender-stereotype color, rankin ... 9 -0.1612 [-0.5655; 0.2432] 0.1143 0.3380 15.54 48.5%
## intervention = Gender-stereotyped motivational ... 1 -0.5871 [-1.1538; -0.0205] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 1.59 1 0.2078
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:age`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random) country:age
## S1 -0.2867 [-1.2593; 0.6860] 6.7 Brazil:adolescent
## S2 -0.0965 [-0.8750; 0.6820] 8.9 Brazil:adolescent
## S3 -0.5455 [-1.3658; 0.2749] 8.4 Brazil:adolescent
## S4 -1.4272 [-2.3807; -0.4738] 6.9 Brazil:adult
## S5 0.2143 [-0.4172; 0.8459] 11.1 Brazil:adult
## S6 0.3687 [-0.2637; 1.0011] 11.1 Brazil:adult
## S7 -0.0005 [-0.5445; 0.5435] 12.7 Brazil:adult
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3 China:no-restriction
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6 Brazil:no-restriction
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3 Brazil:adolescence
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country:age = Brazil:adolescent 3 -0.3036 [-0.8941; 0.2870] 0 0 0.61 0.0%
## country:age = Brazil:adult 4 -0.1420 [-1.3689; 1.0848] 0.3981 0.6310 10.35 71.0%
## country:age = China:no-restriction 1 0.3478 [-0.3995; 1.0950] -- -- 0.00 --
## country:age = Brazil:no-restriction 1 -0.5279 [-1.0772; 0.0214] -- -- 0.00 --
## country:age = Brazil:adolescence 1 -0.5871 [-1.1538; -0.0205] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 4.69 4 0.3204
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random) country:ed.level
## S1 -0.2867 [-1.2593; 0.6860] 6.7 Brazil:upper-secundary
## S2 -0.0965 [-0.8750; 0.6820] 8.9 Brazil:upper-secundary
## S3 -0.5455 [-1.3658; 0.2749] 8.4 Brazil:upper-secundary
## S4 -1.4272 [-2.3807; -0.4738] 6.9 Brazil:higher-education
## S5 0.2143 [-0.4172; 0.8459] 11.1 Brazil:higher-education
## S6 0.3687 [-0.2637; 1.0011] 11.1 Brazil:higher-education
## S7 -0.0005 [-0.5445; 0.5435] 12.7 Brazil:unknown
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3 China:unknown
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6 Brazil:unknown
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3 Brazil:upper-secundary
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country:ed.level = Brazil:upper-secundary 4 -0.4244 [-0.7977; -0.0511] 0 0 1.16 0.0%
## country:ed.level = Brazil:higher-education 3 -0.2277 [-2.6367; 2.1813] 0.7507 0.8664 10.35 80.7%
## country:ed.level = Brazil:unknown 2 -0.2628 [-3.6133; 3.0878] 0.0613 0.2476 1.79 44.1%
## country:ed.level = China:unknown 1 0.3478 [-0.3995; 1.0950] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 3.90 3 0.2727
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random)
## S1 -0.2867 [-1.2593; 0.6860] 6.7
## S2 -0.0965 [-0.8750; 0.6820] 8.9
## S3 -0.5455 [-1.3658; 0.2749] 8.4
## S4 -1.4272 [-2.3807; -0.4738] 6.9
## S5 0.2143 [-0.4172; 0.8459] 11.1
## S6 0.3687 [-0.2637; 1.0011] 11.1
## S7 -0.0005 [-0.5445; 0.5435] 12.7
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3
## country:intervention
## S1 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau
## country:intervention = Brazil:Gender-stereotype color, ... 8 -0.2226 [-0.6616; 0.2164] 0.1182 0.3438
## country:intervention = China:Gender-stereotype color, ... 1 0.3478 [-0.3995; 1.0950] -- --
## country:intervention = Brazil:Gender-stereotyped motiv ... 1 -0.5871 [-1.1538; -0.0205] -- --
## Q I^2
## country:intervention = Brazil:Gender-stereotype color, ... 13.77 49.2%
## country:intervention = China:Gender-stereotype color, ... 0.00 --
## country:intervention = Brazil:Gender-stereotyped motiv ... 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 3.82 2 0.1478
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `age:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random)
## S1 -0.2867 [-1.2593; 0.6860] 6.7
## S2 -0.0965 [-0.8750; 0.6820] 8.9
## S3 -0.5455 [-1.3658; 0.2749] 8.4
## S4 -1.4272 [-2.3807; -0.4738] 6.9
## S5 0.2143 [-0.4172; 0.8459] 11.1
## S6 0.3687 [-0.2637; 1.0011] 11.1
## S7 -0.0005 [-0.5445; 0.5435] 12.7
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3
## age:intervention
## S1 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4 adult:Gender-stereotype color, ranking, badges, and avatar
## S5 adult:Gender-stereotype color, ranking, badges, and avatar
## S6 adult:Gender-stereotype color, ranking, badges, and avatar
## S7 adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs adolescence:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q
## age:intervention = adolescent:Gender-stereotype co ... 3 -0.3036 [-0.8941; 0.2870] 0 0 0.61
## age:intervention = adult:Gender-stereotype color, ... 4 -0.1420 [-1.3689; 1.0848] 0.3981 0.6310 10.35
## age:intervention = no-restriction:Gender-stereotyp ... 2 -0.1282 [-5.6703; 5.4138] 0.2715 0.5210 3.42
## age:intervention = adolescence:Gender-stereotyped ... 1 -0.5871 [-1.1538; -0.0205] -- -- 0.00
## I^2
## age:intervention = adolescent:Gender-stereotype co ... 0.0%
## age:intervention = adult:Gender-stereotype color, ... 71.0%
## age:intervention = no-restriction:Gender-stereotyp ... 70.8%
## age:intervention = adolescence:Gender-stereotyped ... --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 1.27 3 0.7352
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random)
## S1 -0.2867 [-1.2593; 0.6860] 6.7
## S2 -0.0965 [-0.8750; 0.6820] 8.9
## S3 -0.5455 [-1.3658; 0.2749] 8.4
## S4 -1.4272 [-2.3807; -0.4738] 6.9
## S5 0.2143 [-0.4172; 0.8459] 11.1
## S6 0.3687 [-0.2637; 1.0011] 11.1
## S7 -0.0005 [-0.5445; 0.5435] 12.7
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3
## ed.level:intervention
## S1 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7 unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs upper-secundary:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau
## ed.level:intervention = upper-secundary:Gender-stereoty ... 3 -0.3036 [-0.8941; 0.2870] 0 0
## ed.level:intervention = higher-education:Gender-stereot ... 3 -0.2277 [-2.6367; 2.1813] 0.7507 0.8664
## ed.level:intervention = unknown:Gender-stereotype color ... 3 -0.1035 [-1.1720; 0.9651] 0.0854 0.2922
## ed.level:intervention = upper-secundary:Gender-stereoty ... 1 -0.5871 [-1.1538; -0.0205] -- --
## Q I^2
## ed.level:intervention = upper-secundary:Gender-stereoty ... 0.61 0.0%
## ed.level:intervention = higher-education:Gender-stereot ... 10.35 80.7%
## ed.level:intervention = unknown:Gender-stereotype color ... 3.80 47.4%
## ed.level:intervention = upper-secundary:Gender-stereoty ... 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 1.63 3 0.6527
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:age:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random)
## S1 -0.2867 [-1.2593; 0.6860] 6.7
## S2 -0.0965 [-0.8750; 0.6820] 8.9
## S3 -0.5455 [-1.3658; 0.2749] 8.4
## S4 -1.4272 [-2.3807; -0.4738] 6.9
## S5 0.2143 [-0.4172; 0.8459] 11.1
## S6 0.3687 [-0.2637; 1.0011] 11.1
## S7 -0.0005 [-0.5445; 0.5435] 12.7
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3
## country:age:intervention
## S1 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:adolescence:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 3 -0.3036 [-0.8941; 0.2870] 0 0
## country:age:intervention = Brazil:adult:Gender-stereotype ... 4 -0.1420 [-1.3689; 1.0848] 0.3981 0.6310
## country:age:intervention = China:no-restriction:Gender-ste ... 1 0.3478 [-0.3995; 1.0950] -- --
## country:age:intervention = Brazil:no-restriction:Gender-st ... 1 -0.5279 [-1.0772; 0.0214] -- --
## country:age:intervention = Brazil:adolescence:Gender-stere ... 1 -0.5871 [-1.1538; -0.0205] -- --
## Q I^2
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 0.61 0.0%
## country:age:intervention = Brazil:adult:Gender-stereotype ... 10.35 71.0%
## country:age:intervention = China:no-restriction:Gender-ste ... 0.00 --
## country:age:intervention = Brazil:no-restriction:Gender-st ... 0.00 --
## country:age:intervention = Brazil:adolescence:Gender-stere ... 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 4.69 4 0.3204
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for men in stFemale
##
## SMD 95%-CI %W(random)
## S1 -0.2867 [-1.2593; 0.6860] 6.7
## S2 -0.0965 [-0.8750; 0.6820] 8.9
## S3 -0.5455 [-1.3658; 0.2749] 8.4
## S4 -1.4272 [-2.3807; -0.4738] 6.9
## S5 0.2143 [-0.4172; 0.8459] 11.1
## S6 0.3687 [-0.2637; 1.0011] 11.1
## S7 -0.0005 [-0.5445; 0.5435] 12.7
## S8: Conducted by BNU 0.3478 [-0.3995; 1.0950] 9.3
## S9: Albuquerque, et al. (2017) -0.5279 [-1.0772; 0.0214] 12.6
## S10: Only use prompt msgs -0.5871 [-1.1538; -0.0205] 12.3
## country:ed.level:intervention
## S1 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:upper-secundary:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 362
##
## SMD 95%-CI t p-value
## Random effects model -0.2131 [-0.5787; 0.1524] -1.32 0.2198
##
## Quantifying heterogeneity:
## tau^2 = 0.1119 [0.0000; 0.8282]; tau = 0.3344 [0.0000; 0.9100]
## I^2 = 49.0% [0.0%; 75.3%]; H = 1.40 [1.00; 2.01]
##
## Test of heterogeneity:
## Q d.f. p-value
## 17.65 9 0.0395
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 3 -0.3036 [-0.8941; 0.2870] 0
## country:ed.level:intervention = Brazil:higher-education:Gender- ... 3 -0.2277 [-2.6367; 2.1813] 0.7507
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ... 2 -0.2628 [-3.6133; 3.0878] 0.0613
## country:ed.level:intervention = China:unknown:Gender-stereotype ... 1 0.3478 [-0.3995; 1.0950] --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 1 -0.5871 [-1.1538; -0.0205] --
## tau Q I^2
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 0 0.61 0.0%
## country:ed.level:intervention = Brazil:higher-education:Gender- ... 0.8664 10.35 80.7%
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ... 0.2476 1.79 44.1%
## country:ed.level:intervention = China:unknown:Gender-stereotype ... -- 0.00 --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 3.88 4 0.4222
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.cont <- update.meta(m.cont, studlab = data$study)
summary(eggers.test(x = m.cont))## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## -1.585 -6.11 - 2.94 -0.686 0.51
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.funnel(m.cont, xlab = "Hedges' g", studlab = T, legend=T, addtau2 = T)